Drive-time vs. radius catchments: when each is right

Almost every site package we review opens with the same exhibit: three concentric rings around a pin, usually 1, 3 and 5 miles, with population and median income stacked beside them. It is the lingua franca of retail real estate, and it is genuinely useful. It is also routinely asked to answer questions it was never built for.

Since drive-time catchments landed in MapDove, we get a version of the same question every week: should we switch everything to drive time? The short answer is no. The longer answer is that radius and drive-time rings measure different things, and a good analyst picks the one that matches the decision in front of them.

What a radius ring actually measures

A radius ring is a statement about proximity, not access. It says: these are the people who live within a fixed straight-line distance of the site. Its strengths are real:

• It is comparable. A 3-mile ring in Austin and a 3-mile ring in Miami are the same physical size, so densities and incomes compare cleanly across markets.
• It is stable. It does not change when a road is widened or when you change your assumptions about traffic.
• It matches how deals are written. Radius restrictions in leases, exclusives and co-tenancy clauses are almost always expressed in miles or kilometers.

Its weakness is equally clear. It ignores how people move. A ring that crosses a river, a freeway with few interchanges or a large industrial district counts households that, in practice, will rarely make the trip.

What a drive-time ring measures

A drive-time ring is a statement about effort. It asks: who can reach this site in 5, 10 or 15 minutes? That framing is closer to how convenience-driven categories behave. Nobody picks a pharmacy or a quick-service lunch based on crow-flies distance; they pick the one that is easy to get to.

Drive time also normalizes across cities with very different travel speeds. Ten minutes of driving covers a lot more ground in a fast, car-oriented metro than in a dense, congested one, and that difference matters when you are sizing a trade area.

How MapDove computes drive-time rings (and what that implies)

We want to be precise about this, because it affects how you should use the output. MapDove’s drive-time mode is a speed-based approximation, not a road-network isochrone. When you request rings in minutes, we convert each ring to a radius using the city’s average travel speed:

City	Avg speed	10-minute ring ≈
Austin	42 km/h	7.0 km (4.3 mi)
Houston	45 km/h	7.5 km (4.7 mi)
Phoenix	50 km/h	8.3 km (5.2 mi)
Miami	35 km/h	5.8 km (3.6 mi)
Dubai	55 km/h	9.2 km

Demographics inside each ring are area-weighted from the underlying hex grid (cells of roughly 0.8 km²): if a ring covers 40% of a hex, it picks up 40% of that hex’s population and households. You can request up to four rings per analysis, in km, mi or minutes, through the app or the /sdss/catchment endpoint.

What this approximation does well: it scales catchments sensibly between markets. A 10-minute ring in Miami is meaningfully smaller than one in Phoenix, which is exactly the adjustment a planner would make by hand.

What it does not do: it will not bend around a bay, follow a freeway corridor or stop at a river without a bridge. If your site sits next to a hard barrier, treat the drive-time ring as an upper bound and sanity-check it against the modeled trade area, which is built from where visitors actually come from in the demo dataset.

Choosing between them

A practical rule of thumb we use internally:

Use radius rings when

• You are comparing markets or screening dozens of candidate sites on a level playing field.
• The question is about lease language: radius restrictions, exclusives, co-tenancy.
• You need a figure that a landlord, lender or investment committee will recognize instantly.
• The category is destination-driven (cinemas, furniture, department stores) and customers routinely travel past competitors.

Use drive-time rings when

• The category is convenience-driven: coffee, pharmacy, fuel, quick-service, grocery top-up trips.
• You are comparing sites across cities with different travel speeds and want “equivalent effort” catchments.
• You are setting network spacing and want to know roughly how far apart stores can be before the gap becomes inconvenient.
• You are pairing the catchment with a Huff forecast, which already reasons in terms of distance decay.

Use neither on its own when

The decision is large and the site is unusual. Rings are a first pass. For a final recommendation, layer the ring demographics with the modeled trade area, the suitability score for the hex, and the cannibalization estimate against your own network.

A worked example

Consider a hypothetical new Bean Republic location on the edge of The Domain in Austin. A 3-mile radius ring and a 10-minute drive-time ring (about 4.3 miles at Austin’s average speed) look similar on a map. But the drive-time ring pulls in a meaningful slice of the higher-income suburbs to the north and west, which a coffee operator cares about because commute-adjacent morning trips are the core of the business.

Now run the same exercise for a Starlight Cinemas candidate. For a destination category, the 10-minute ring understates reach; moviegoers will drive 20 minutes for the right screen. Here a 5-mile radius plus the modeled trade area is the more honest picture.

Same tool, same pin, different right answer.

Common mistakes

• Mixing units in one deck. Show either miles or minutes for a given comparison, never both, or readers will compare the wrong numbers.
• Treating drive time as precise. A speed-based ring is a planning approximation. Present it as “approximately 10 minutes,” not “exactly 10 minutes.”
• Using the same ring sizes for every category. A 1-mile ring is meaningful for a dense urban pharmacy and nearly meaningless for a regional fitness club.

Takeaway

Use radius rings to compare and to negotiate; use drive-time rings to reason about convenience and equivalent effort across cities. In MapDove, drive time is a transparent speed-based conversion with area-weighted demographics, so check it against the modeled trade area wherever geography is lumpy. Try both on the same pin in the catchment tool and see where they disagree; that disagreement is usually where the insight is.